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This week, Waymo raised $2.25 billion from outside investors led by the private equity firm Silver Lake, the Canada Pension Plan Investment Board, and Mubadala, a sovereign wealth fund of the United Arab Emirates. Additional investors included the venture capital firm Andreessen Horowitz, the car dealer AutoNation, the contract carmaker and auto parts maker Magna, and Waymo’s parent company Alphabet.

According to reporter Richard Waters at The Financial Times, the investment round valued Waymo at $30 billion. By comparison, Cruise, a subsidiary of General Motors, is valued at $19 billion by its outside investors, which include SoftBank, Honda, and T. Rowe Price.

It seems increasingly likely to me that Waymo has internal metrics that show its driverless robotaxis in the Phoenix, Arizona metro area have superhuman safety. Hopefully, Waymo will be able to publicly confirm this hunch by the end of 2020:

Cruise, for its part, seems confident it can cross the human-level threshold within the next few years:

The last concrete information we got out of Cruise was an internal report that was leaked to the press in June. The report projected (it’s unclear on what basis) that Cruise would be at 5% to 11% of human-level safety by the end of 2019.

The upshot is that robotaxi companies that are perceived to be global leaders are getting valuations of ~$20 billion or $30 billion, even given the uncertainty, skepticism, and sense of risk that pervades today. Here are what I see as the next steps for robotaxi companies:

Show a positive gross margin that demonstrates a path to long-term net profitability.

Devise a credible plan to rapidly scale up service to the level of nations and continents.

If those four criteria are satisfied, then I believe we’re looking at a scenario where the global robotaxi market is worth $1 trillion+ collectively, as the equity research firm ARK Invest models:

Autonomous cars are, of course, robots that use deep learning: to perceive the environment, to predict the future, and to plan actions. The performance of deep learning scales with data, sometimes in predictable, lawlike ways. Baidu conducted research that found, for image recognition, accuracy scales roughly 2x with each 10x increase in data. So, let’s use this knowledge to do a comparison between companies.

In October 2018, Waymo announced its fleet had driven 10 million miles cumulatively. Fourteen months later, in January 2020, it announced it hit 20 million miles. That’s 715,000 miles per month.

Tesla has a fleet of roughly 800,000 cars equipped with 360-degree cameras, a forward-facing radar, ultrasonics, and either a) the “Hardware 2” computer supplied by Nvidia or b) the ~10-20x more powerful Full Self-Driving Computer designed in-house by Tesla (also known as “Hardware 3”). My rough guess is that approximately 400,000 cars have the FSD Computer. Let’s assume these 400,000 cars drive 37 miles per day on average. That’s 440 million miles per month or about 620x more than Waymo. Including all 800,000 cars, Tesla’s drives over 1,200x as much as Waymo.

With the scaling rate discovered by Baidu, 620x more data would translate into about 6x better performance (if my math is correct.). 1,200x more data would rest in more than 8x better performance. In my opinion, because investors and analysts don’t appreciate this fact, Tesla is radically mispriced as a robotaxi company relative to Waymo and Cruise. I could be wrong, but as far as I know, investors and analysts broadly attribute almost $0 in value to Tesla as a robotaxi company. (Please email me if you think this might be incorrect.)

It’s true that for what’s known as fully supervised deep learning of computer vision tasks, the bottleneck is manual labelling, rather than miles driven. However, this is not true for self-supervised, semi-supervised, or weakly supervised learning of computer vision tasks. It’s also not true for prediction tasks or planning tasks at all. Moreover, the quality of data used in fully supervised learning scales with miles driven. Companies employ a variety of techniques to automatically curate the most valuable data from their fleets. The more miles driven, the more value. (See an elaboration on all these concepts in my blog post here.)

For example, consider a rare type of wildlife like moose or bears. Or a rare vehicle type like an excavator or tanker truck. A fleet of cars that drives 620x more will encounter 620x more moose, bears, excavators, and tanker trucks. If these objects are rare enough that the bottleneck is finding enough new examples to label, then Tesla’s performance on object recognition for these rare objects will scale with its miles driven. 620x more examples will lead to 6x better performance.

As I see it, in their pricing of Waymo, Cruise, and Tesla, the markets are neglecting a fact of computer science. I think the narrative around Tesla and autonomy will profoundly shift once Tesla finishes its rewrite of Autopilot and ships it to customers. I expect that will most likely happen before the end of this year. The underlying computer science principles, which remain unappreciated by market participants, will translate into visible progress in the production Autopilot system that is used by hundreds of thousands of customers. At that point, I suspect many of Wall Street’s sell-side analysts will scramble to update their views and begin citing Tesla’s data advantage.

Financial disclosure: I own shares of Tesla (TSLA).

Important disclaimer: This newsletter is not intended as financial advice. Invest at your own risk and please consult a professional investment advisor if that is appropriate to your situation.

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